Data mining and statistics for decision making

by Tuffery, Stéphane

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Title Statements

Full title :Data mining and statistics for decision making / Stéphane Tufféry ; translated by Rod Riesco.

Summary

"This practical guide to understanding and implementing data mining techniques discusses traditional methods--cluster analysis, factor analysis, linear regression, PLS regression, and generalized linear models--and recent methods--bagging and boosting, decision trees, neural networks, support vector machines, and genetic algorithm. The book focuses largely on credit scoring, one of the most common applications of predictive techniques, but also includes other descriptive techniques, such as customer segmentation. It also covers data mining with R, provides a comparison of SAS and SPSS, and includes an appendix presenting the necessary statistical background"--

"Data Mining is a practical guide to understanding and implementing data mining techniques, featuring traditional methods such as cluster analysis, factor analysis, linear regression, PLS regression and generalised linear models"--

Edition Statements

Publication Date:2011.

Publication Statements

Publisher(s):Wiley
Place of Publication:Chichester, West Sussex, U.K
Language:eng
Publication Date:2011.

Notes

In IT Pro / ITPro Collection.

Titre de l'écran-titre.

Traduction de: Data mining et statistique décisionnelle.

Versement en lot.

The Physical Object

Description:1 online resource (1 texte électronique).

Edition Identifiers

ISBN:9780470979167, 047097916X, 9780470979174, 0470979178, 9780470979280, 0470979283, 0470688297, 9780470688298
oclc:ocn759912607
LC Classification:QA76.9.D343
Dewey Decimal:006.3/12

Front Matter -- Overview of Data Mining -- The Development of a Data Mining Study -- Data Exploration and Preparation -- Using Commercial Data -- Statistical and Data Mining Software -- An Outline of Data Mining Methods -- Factor Analysis -- Neural Networks -- Cluster Analysis -- Association Analysis -- Classification and Prediction Methods -- An Application of Data Mining: Scoring -- Factors for Success in a Data Mining Project -- Text Mining -- Web Mining -- Appendix A: Elements of Statistics -- Appendix B: Further Reading -- Index.

Machine generated contents note: Preface -- Foreword -- Contents -- Overview of data mining -- 1.1. What is data mining? -- 1.2. What is data mining used for? -- 1.3. Data Mining and statistics -- 1.4. Data mining and information technology -- 1.5. Data mining and protection of personal data -- 1.6. Implementation of data mining -- The development of a data mining study -- 2.1. Defining the aims -- 2.2. Listing the existing data -- 2.3. Collecting the data -- 2.4. Exploring and preparing the data -- 2.5. Population segmentation -- 2.6. Drawing up and validating predictive models -- 2.7. Synthesizing predictive models of different segments -- 2.8. It…